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Adding colors and legends to graphs and charts helps to make data easier to understand. Bokeh makes the process easy.
Code for color_mapper:
color_mapper = CategoricalColorMapper(factors=['Asia', 'Africa', 'Antarctica', 'Australia', 'Central America', 'Europe', 'North America', 'Oceania', 'South America'], palette=['#00FF00', '#FFD343', 'darkgray', 'brown', 'cyan', 'crimson', 'red', '#0000FF', 'purple'])
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When we were last looking at the data,
0:00
we were using a scatter plot to see
if there were any correlation between
0:02
a country's population and life expectancy
from our world population dataset.
0:05
We used Bokeh column data source to
map our data into usable columns and
0:09
were able to generate the scatter plot
to get a better idea of any relationship
0:13
between life expectancy and population.
0:17
There are couple of issues
with our last plot though.
0:19
Everything was cluttered and
same color, and nothing was labeled.
0:22
Obviously, this makes it a challenge
to come with any conclusions about our
0:26
dataset.
0:30
Bokeh includes some great tools for
this which allow us to map colors to
0:31
specific items or categories of items to
quickly add legends to our visualizations.
0:36
If we remember back when we looked at the
headers in our data set, one of the pieces
0:43
of data we get about each country is the
continent on which the country resides.
0:47
It might be nice,
0:51
when we're trying to figure out
a correlation between life expectancy and
0:52
population, if region of the world
leads to any insightful information.
0:55
One of the functions Bokeh provides
is categorical color mapper.
1:01
Which as the name would indicate
maps colors to different
1:05
categorical data buckets.
1:09
This allows to utilize
different categories or
1:11
in Bokeh terms factors which
in our case are countries and
1:13
assigned certain color values,
a palette to each category.
1:17
One of the functions Bokeh provides
is categorical color mapper, which,
1:22
as the name would indicate, maps colors
to different categorical data buckets.
1:28
This allows us to utilize
different categories, or
1:33
in Bokeh's terms, factors,
which in our case are countries, and
1:36
assigned certain color values,
palette, to each category.
1:39
The colors we pass into the categorical
color mapper function can be any valid CSS
1:44
or hex color,
meaning we can use either green, or
1:49
your favorite #00FF00 for our value.
1:53
Let's jump back into our project and add
some colors to our continent buckets and
1:57
then show how we can add a legend to our
plot to add context to the information.
2:01
Here's where we left off at
the end of our last video.
2:06
To get started adding some color, we need
to add a color mapper to our code so
2:08
that we can assign specific
colors to our continents.
2:12
We'll import categorical color mapper from
bokeh.models, then create our color mapper
2:16
with our continent buckets or factors,
and then assign each factor a color.
2:22
From bokeh.models import
2:26
CategoricalColorMapper.
2:31
Now, we look at all of the continent
options in our entire data set,
2:36
we see that we have nine different buckets,
Asia, Africa, Antarctica,
2:40
Australia, Central America, Europe,
North America, Oceana, and South America.
2:45
We pass those in as a list
into the factors argument
2:52
of CategoricalColorMapper.
2:54
Then in a matching linked list, pass
in our colors to the palette argument.
2:56
I've already typed this out and
I'll paste it in here.
3:00
I've included that in the teacher's notes
to save you from having to type it
3:10
in as well.
3:13
Then we can use our color
mapper in our plot.
3:14
We do that by adding another
argument one we call plot.circle.
3:17
Or whatever glyph we're using in
our plot with the color parameter.
3:20
We'll pass into the color
parameter a dictionary and
3:24
which field we are basing our color mapper
on and the associated color mapper.
3:28
At this point we should remove our
five country limitation though so
3:33
we can see all of
the countries represented.
3:37
Let's also, just for fun, change our
glyph from a circle to a diamond.
3:40
Just to show some other available glyphs.
3:45
We'll also adjust the size
of our glyph down slightly.
3:47
Then when we show our plot and
3:50
run our script,
we should see our data separated by color.
3:52
So we'll change circle to diamond.
3:55
Change our size down to 10.
4:02
And then add in our color
parameter which is dict.
4:06
Based on the continent field and
4:12
a transform parameter is our color_mapper.
4:18
Now, we should be able to run our script.
4:29
Nice, we have our data
split out into buckets and
4:32
associated with each other by color based
on which continent a country resides.
4:35
We are still missing a way to know which
color represents which continent though.
4:40
We need a legend for our plot.
4:43
Fortunately that is easily
done with Bokeh by passing in
4:45
a legend parameter with the column name
that we want to use in the legend.
4:49
In this case continent.
4:53
So we can add our legend.
4:54
And base it on our continent field.
5:01
And run our script again.
5:05
Well that certainly helps but
5:09
it is sitting right on top
of some of our data points.
5:11
Let’s adjust the location
of the legend and
5:14
move it to the bottom right of our
plot and add a background color to it
5:16
as well to help it stand out visually
before we show the plot again.
5:20
We can do that by adjusting
the plot.legend and
5:23
assigning it a location, bottom_right.
5:28
And the background fill_color.
5:39
I will do lightgrey.
5:45
And show our plot again.
5:53
This is looking more better as we
can get a general idea of at least
5:56
which continents the countries are on and
start to put some context to our data.
6:00
This still isn't super
helpful though as our glyphs
6:04
aren't currently tagged in any way
as to which country they represent.
6:07
Let's tackle that in our next video.
6:11
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